This paper delves into the lateral load-bearing behavior of lattice-shaped diaphragm wall (LSDW), a novel type of diaphragm wall foundation with many engineering advantages. By employing a double-layer wall structure for the first time in laboratory settings, the research presents an innovative testing methodology, complete with novel computational formulas, to accurately measure the responses of LSDW’s inner and outer walls under varying loads. It is found that the Q–s curves of LSDWs exhibit a continuous, progressive deformation and failure characteristic without any abrupt drops, and the standard for judging the horizontal bearing capacity of LSDW foundations should be based on the allowable displacement of the superstructure. The bearing capacity for the double-chamber LSDWs was found to be approximately 1.68 times that of the single-chamber structure, pointing to a complex interplay between chamber number and structural capacity that extends beyond a linear relationship and incorporates the group wall effect. The study also reveals that LSDWs act as rigid bodies with minimal angular displacement and a consistent tilting deformation, peaking in bending moment at about 0.87 of wall depth from the mud surface, across different chamber configurations. Furthermore, it can be found that using the p–y curve method for analyzing the horizontal behavior of LSDW foundations is feasible, and the hyperbolic p–y curve method offers higher accuracy in calculations. These insights offer valuable guidance for both field and laboratory testing of LSDWs and aid in the design and calculation of foundations under horizontal loads.

Digital fabrication techniques, in recent decades, have provided the basis of a sustainable revolution in the construction industry. However, selecting the digital fabrication method in terms of manufacturability and functionality requirements is a complex problem. This paper presents alternatives and criteria for selection of digital fabrication techniques by adopting the multi-criteria decision-making technique. The alternatives considered in the study are concrete three-dimensional (3D) printing, shotcrete, smart dynamic casting, material intrusion, mesh molding, injection concrete 3D printing, and thin forming techniques. The criteria include formwork utilization, reinforcement incorporation, geometrical complexity, material enhancement, assembly complexity, surface finish, and build area. It demonstrates different multi-criteria decision-making techniques, with both subjective and objective weighting methods. The given ranking is based on the current condition of digital fabrication in the construction industry. The study reveals that in the selection of digital fabrication techniques, the criteria including reinforcement incorporation, build area, and geometrical complexity play a pivotal role, collectively accounting for nearly 70% of the overall weighting. Among the evaluated techniques, concrete 3D printing emerged as the best performer, however the shotcrete and mesh molding techniques in the second and third positions.

This paper proposes an accurate, efficient and explainable method for the classification of the surrounding rock based on a convolutional neural network (CNN). The state-of-the-art robust CNN model (EfficientNet) is applied to tunnel wall image recognition. Gaussian filtering, data augmentation and other data pre-processing techniques are used to improve the data quality and quantity. Combined with transfer learning, the generality, accuracy and efficiency of the deep learning (DL) model are further improved, and finally we achieve 89.96% accuracy. Compared with other state-of-the-art CNN architectures, such as ResNet and Inception-ResNet-V2 (IRV2), the presented deep transfer learning model is more stable, accurate and efficient. To reveal the rock classification mechanism of the proposed model, Gradient-weight Class Activation Map (Grad-CAM) visualizations are integrated into the model to enable its explainability and accountability. The developed deep transfer learning model has been applied to support the tunneling of the Xingyi City Bypass in the high mountain area of Guizhou, China, with great results.

As urban construction continues to develop and automobile ownership rises, parking shortages in cities have become increasingly acute. Given the limited availability of land resources, conventional underground garages and parking buildings no longer suffice to meet the growing demand for parking spaces. To address this dilemma, underground parking shaft (UPS) has emerged as a highly regarded solution. This study provides an overview of the layout scheme, structural design approaches, and construction techniques for UPS, focusing on the characteristics of intensive construction demonstrated in the project located in the Jianye District of Nanjing. Compared to conventional vertical shaft garage construction methods, this assembly parking shaft offers advantages such as a smaller footprint, higher prefabrication rate, shorter construction period, and reduced environmental impact. It presents an efficient approach for the intensive construction of urban underground spaces, particularly in areas with limited land and complex environments, showing promising prospects for widespread application.

Seismic resistance systems for small and mid-span girder bridges often lacks hierarchically repeatable earthquake resistance, leading to challenging and time-consuming post-earthquake repairs. This research introduces a novel quasi-floating seismic resistance system (QFSRS) with hierarchically sacrificial components to enable multiple instances of earthquake resistance and swift post-earthquake restoration. Finite element modeling, a numerical probabilistic approach, and earthquake-simulating shake-table tests identified highly sensitive parameters from the QFSRS to establish theoretical equations describing the mechanical model and working mechanism of the system. The results indicate that the working mechanism of the QFSRS under seismic conditions aligns with the theoretical design, featuring four hierarchically sacrificial seismic stages. Specifically, under moderate (0.3g) or higher seismic conditions, QFSRS reduced relative displacement between piers and beams by 55.15% on average. The strain at pier bases increased 6.17% across all seismic scenarios, significantly enhancing bridge seismic performance. The QFSRS provides resilient and restorable earthquake resistance for girder bridges.

Concrete is the most widely utilized material for construction purposes, second only to water, in the ever-increasing need for construction globally. Concrete is a brittle material and possesses a high risk of crack formation and consequent deterioration. Cracking, which allows chemicals to enter and can cause concrete structures to lose their physico-mechanical and durability features. Repairing and rehabilitating concrete structures involves high costs and leads to various repair methods including coating, adhesives, polymers, supplementary cementitious materials (SCMs), and fibers. One of the latest technologies is the use of microorganisms in concrete. These added microorganisms lead to calcite precipitation and thereby heal the cracks effectively. This study presents a comprehensive literature survey on bacteria-included concrete, before which a bibliographic survey is performed using VOSViewer software. In addition to regular bacterial concrete, this study focuses on also using SCMs and fibers in bacterial concrete. A detailed literature review with data representation for various mechanical properties including compressive strength (CS), split tensile strength (SS), and flexure strength (FS), along with durability properties including carbonation, water absorption, resistance against chloride ion penetration, gas permeation, and resistance against cyclic freeze-and-thaw is presented. A study on the use of X-ray computed tomography (XCT) in bacterial concrete is highlighted, and the scope for future research, along with identification of the research gap, is presented.

The mechanical and durability characteristics of concrete are crucial for designing and evaluating concrete structures throughout their entire operational lifespan. The main objective of this research is to use the deep learning (DL) method along with an artificial neural network (ANN) to predict the chloride migration coefficient and concrete compressive strength. An expansive experimental database of nearly 1100 data points was gathered from existing scientific literature. Four forecast models were created, utilizing between 10 and 12 input features. The ANN was used to address the missing data gaps in the literature. A comprehensive pre-processing approach was then implemented to identify outliers and encode data attributes. The use of mean absolute error (MAE) as an evaluation metric for regression tasks and the employment of a confusion matrix for classification tasks were found to produce accurate results. Additionally, both the compressive strength and chloride migration coefficient exhibit a high level of accuracy, above 0.85, in both regression and classification tasks. Moreover, a user-friendly web application was successfully developed in the present study using the Python programming language, improving the ability to integrate smoothly with the user’s device.

Reasonable prediction of concrete creep is the basis of studying long-term deflection of concrete structures. In this paper, a hybrid model-driven and data-driven (HMD) method for predicting concrete creep is proposed by using the sequence integration strategy. Then, a novel uncertainty prediction model (UPM) is developed considering uncertainty quantification. Finally, the effectiveness of the proposed method is validated by using the North-western University (NU) database of creep, and the effect of uncertainty on prediction results are also discussed. The analysis results show that the proposed HMD method outperforms the model-driven and three data-driven methods, including the genetic algorithm-back propagation neural network (GA-BPNN), particle swarm optimization-support vector regression (PSO-SVR) and convolutional neural network only method, in accuracy and time efficiency. The proposed UPM of concrete creep not only ensures relatively good prediction accuracy, but also quantifies the model and measurement uncertainties during the prediction process. Additionally, although incorporating measurement uncertainty into concrete creep prediction can improve the prediction performance of UPM, the prediction interval of the creep compliance is more sensitive to model uncertainty than to measurement uncertainty, and the mean contribution of variance attributed to the model uncertainty to the total variance is about 90%.

In this study, we propose the use of a fiber-reinforced plastic grid with polymer−cement−mortar (FRP Grid-PCM) to reinforce segment joints in tunnel shield linings. These joints play a crucial role in determining bearing capacity but are vulnerable to deterioration during operation. To investigate how to enhance the flexural performance of longitudinal shield lining joints, we built eccentric short column specimens by bolting two half-corbel columns together and tested them in the laboratory. The test program comprised two control specimens and three strengthened specimens with FRP grid applied on one side, away from the axial load. The tests varied two main parameters: loading eccentricity and the number of FRP grid layers. We conducted a detailed analysis of the failure process, bearing capacity, and bending stiffness of longitudinal joints under different conditions. Furthermore, we developed an analytical model to predict the flexural bearing capacity of longitudinal joints upgraded with the FRP Grid-PCM method and validated it through experimental results. The research demonstrates that the FRP grid effectively reduces joint opening and rotation angles while enhancing the bearing capacity of the short column, particularly with concurrent increases in loading eccentricity and the number of FRP grid layers. Overall, our findings offer a novel alternative for improving the flexural performance of longitudinal joints in shield tunnels.

The thixotropic structural build-up is crucial in extrusion-based three-dimensional (3D) concrete printing. This paper uses a theoretical model to predict the evolution of static and dynamic yield stress for printed concrete. The model employs a structural kinetics framework to create a time-independent constitutive link between shear stress and shear rate. The model considers flocculation, deflocculation, and chemical hydration to anticipate structural buildability. The reversible and irreversible contributions that occur throughout the build-up, breakdown, and hydration are defined based on the proposed structural parameters. Additionally, detailed parametric studies are conducted to evaluate the impact of model parameters. It is revealed that the proposed model is in good agreement with the experimental results, and it effectively characterizes the structural build-up of 3D printable concrete.

In slurry shield tunneling, the stability of tunnel face is closely related to the filter cake. The cutting of the cutterhead has negative impact on the formation of filter cake. This study focuses on the formation time of dynamic filter cake considering the filtration effect and rotation of cutterhead. Filtration effect is the key factor for slurry infiltration. A multilayer slurry infiltration experiment system is designed to investigate the variation of filtrate rheological property in infiltration process. Slurry mass concentration C_L, soil permeability coefficient k, the particle diameter ratio between soil equivalent grain size and representative diameter of slurry particles d₁₀/D₈₅ are selected as independent design variables to fit the computational formula of filtration coefficient. Based on the relative relation between the mass of deposited particles in soil pores and infiltration time, a mathematical model for calculating the formation time of dynamic filter cake is proposed by combining the formation criteria and formation rate of external filter cake. The accuracy of the proposed model is verified through existing experiment data. Analysis results show that filtration coefficient is positively correlated with slurry mass concentration, while negatively correlated with the soil permeability coefficient and the particle diameter ratio between soil and slurry. As infiltration distance increases, the adsorption capacity of soil skeleton to slurry particles gradually decreases. The formation time of external filter cake is significantly lower than internal filter cake and the ratio is approximately 3.9. Under the dynamic cutting of the cutterhead, the formation time is positively associated with the rotation speed of cutter head, while negatively with the phase angle difference between adjacent cutter arm. The formation rate of external filter cake is greater than 98% when d₁₀/D₈₅≤ 6.1. Properly increasing the content or decreasing the diameter size of solid-phase particles in slurry can promote the formation of filter cake.

The importance of geometrical control of three dimensional (3D) printable concrete without the support of formwork is widely acknowledged. In this study, a numerical model based on computational fluid dynamics was developed to evaluate the geometrical quality of a 3D printed layer. The numerical results were compared, using image analysis, with physical cross-sectional sawn samples. The influence of printing parameters (printing speed, nozzle height, and nozzle diameter) and the rheological behavior of printed materials (yield stress), on the geometrical quality of one printed layer was investigated. In addition, the yield zone of the printed layer was analyzed, giving insights on the critical factors for geometrical control in 3D concrete printing. Results indicated that the developed model can precisely describe the extrusion process, as well as the cross-sectional quality.

This paper presents a calculation method that evaluates the extent of disturbance based on structural safety limits. Additionally, it summarizes the assessment methods for construction disturbance zones in shield tunneling near pile foundations, urban ground structures, and underground structures. Furthermore, taking the construction of the Chengdu Jinxiu Tunnel under bridges and urban pipelines as the engineering background, a study on the disturbance zoning of adjacent structures was conducted. The most intense disturbance occurs within one week of the tunnel underpass process, and it has a significant impact within a range of two times the tunnel diameter along the tunnel axis. The bridge pile and bridge deck experience less disturbance from tunnel approaching construction, with a maximum disturbance zone characterized as medium disturbance. On the other hand, underground pipelines are subjected to more significant disturbances from tunnel construction, with a maximum disturbance zone classified as strong disturbance. The implementation of “bridge pile sleeve valve pipe grouting & underground pipeline ground grouting & tunnel advance grouting” in the field effectively limits the vertical settlement of bridges and pipelines, resulting in a decrease of approximately 0.1 in disturbance level for the structures. The disturbance zoning method can assess tunnel disturbance with structures, identify high-risk interference locations, and facilitate targeted design reinforcement solutions.

The recycled powder (RP) from construction wastes can be used to partially replace cement in the preparation of reactive powder concrete. In this paper, reactive powder concrete mixtures with RP partially replacing cement, and natural sand instead of quartz, are developed. Standard curing is used, instead of steam curing that is normally requested by standard for reactive powder concrete. The influences of RP replacement ratio (0, 10%, 20%, 30%), silica fume proportion (10%, 15%, 20%), and steel fiber proportion (0, 1%, 2%) are investigated. The effects of RP, silica fume, and steel fiber proportion on compressive strength, elastic modulus, and relative absorption energy are analyzed, and theoretical models for compressive strength, elastic modulus, and relative absorption energy are established. A constitutive model for the uniaxial compressive stress–strain relationship of reactive powder concrete with RP is developed. With the increase of RP replacement ratio from 0% to 30%, the compressive strength decreases by 42% and elastic modulus decreases by 24%.

This study develops a machine-based washing and sieving method to accurately determine the soil particle size distribution for classification. This machine-based method is an extension of the recently developed and invented manual-based extended wet sieving method. It revises and upgrades a conventional rotary vibrating sieve machine with a steel sieve of aperture 0.063 mm and ten cloth sieves of apertures from 0.048 to 0.0008 mm for washing and sieving silt and clay. The machine generates three-dimensional motion and vibration, which allows particles smaller than the sieve aperture to pass through the sieve quickly. A common soil in Hong Kong, China, named completely decomposed tuff soil is used as test material for illustration. The silt and clay mixtures are successfully separated into many sub-groups of silt particles and clay particles from 0.063 to less than 0.0008 mm. The test results of the machine-based method are examined in detail and also compared with the manual-based method. The results demonstrate that the machine-based method can shorten the sieving duration and maintain high accuracy. The particle sizes of separated silt and clay particles are further examined with scanning electron microscopic images. The results further demonstrate that the machine-based method can accurately separate the particles of silt and clay with the pre-selected sieve sizes. This paper introduces a new machine-based washing and sieving method, and verifies the efficiency of the machine-based method, the accuracy of particle size, and its applicability to the classification of different types of soil.

Civil infrastructure is prone to structural damage due to high geo-stress and other natural disasters, so monitoring is required. Data collected by structural health monitoring (SHM) systems are easily affected by many factors, such as temperature, sensor fluctuation, sensor failure, which can introduce a lot of noise, increasing the difficulty of structural anomaly identification. To address this problem, this paper designs a new process of structural anomaly identification under noisy conditions and offers Civil Infrastructure Denoising Autoencoder (CIDAE), a denoising autoencoder-based deep learning model for SHM of civil infrastructure. As a case study, the effectiveness of the proposed model is verified by experiments on deformation stress data of the Wuhan Yangtze River Tunnel based on finite element simulation. Investigation of the circumferential weld and longitudinal weld data of the case study is also conducted. It is concluded that CIDAE is superior to traditional methods.

Inverse problem-solving methods have found applications in various fields, such as structural mechanics, acoustics, and non-destructive testing. However, accurately solving inverse problems becomes challenging when observed data are incomplete. Fortunately, advancements in computer science have paved the way for data-based methods, enabling the discovery of nonlinear relationships within diverse data sets. In this paper, a step-by-step completion method of displacement information is introduced and a data-driven approach for predicting structural parameters is proposed. The accuracy of the proposed approach is 23.83% higher than that of the Genetic Algorithm, demonstrating the outstanding accuracy and efficiency of the data-driven approach. This work establishes a framework for solving mechanical inverse problems by leveraging a data-based method, and proposes a promising avenue for extending the application of the data-driven approach to structural health monitoring.

Using the complex variable method, an elastic analytical solution of the ground displacement caused by a shallow circular tunneling is derived. Non-symmetric deformation relative to the horizontal center line of the tunnel cross-section is used as a boundary condition. A comparison between the proposed analytical method and the Finite Element Method is carried out to validate the rationality of the obtained analytical solution. Two parameters in the Peck formula, namely the maximum settlement of the ground surface center and the width coefficient of settlement curve, are fitted and determined. We propose a modified Peck formula by considering three input parameters, namely the tunnel depth, tunnel radius, and the tunnel gap. The influence of these three parameters on the modified Peck formula is analyzed. The applicability of the modified Peck formula is further investigated by reference to six engineering projects. The ground surface displacement obtained by the explicit Peck formula is in good agreement with the field data, and the maximum error is only 1.3 cm. The proposed formula can quickly and reasonably predict the ground surface settlement caused by tunnelling.

In this article, the mechanical properties of tunnel joints with curved bolts are studied and analyzed using the research methods of full-scale testing and finite element numerical simulation. First, the experiment results were analyzed to find out the development law of stress and strain of concrete in each part of the tunnel fragment when bearing. The damage process of the joint of the tunnel fragment was described in stages, and the characteristic load value that can reflect the initial bearing capacity in each stage was proposed. Afterward, using the ABAQUS three-dimensional (3D) finite element numerical modeling software, a numerical model corresponding to the experiment was established. The mid-span deflection was used to observe the change in loading and the destruction of each stage, comparing it to the proposed form to verify the reasonableness of the numerical model. Finally, the numerical models were used to analyze the change in material parameters and external loads from two aspects. It is concluded that the damage process of tunnel joints under curved bolt connection can be divided into concrete elasticity stage, inner arc cracking stage, overall joint damage stage, and ultimate joint damage stage, and the initial load of the adjacent stages is defined as the characteristic load value. After concrete cracking occurs, the bolts start to become the main load-bearing components, and the bolt stress grows rapidly in stage II. The strain development of the concrete on the outer arc is greater than the strain value of the concrete on the side due to mutual contact and extrusion. The parameters were changed for material properties, and it was found that increasing the concrete strength and bolt strength could improve the shield fragment joint bearing performance. The optimal effect of improving the mechanical properties of the shield fragment joint would be obtained when the concrete strength grade is C60, and the bolt strength grade is 8.8. Increasing the size of the axial force and bolt preload has the most obvious effect on the load-carrying capacity in the initial elastic phase. This can reduce the joint angle and thus improve joint stiffness. Meanwhile, increasing the axial force has a greater effect on the performance of the tunnel joint than the bolt preload.

Dynamic soil−pile−superstructure interaction is crucial for understanding pile behavior in earthquake-prone ground. Evaluating the safety of piles requires determining the seismic bending moment caused by combined inertial and kinematic interactions, which is challenging. This paper addresses this problem through numerical simulations of piles in different soil sites, considering soil nonlinearity. Results reveal that the period of the soil site significantly affects the interaction among soil, piles, and structures. Bending moments in soft and hard soil sites exceed those in medium soil sites by more than twice. Deformation modes of piles exhibit distinct characteristics between hard and soft soil sites. Soft soil sites exhibit a singular inflection point, while hard soil sites show two inflection points. In soft soil sites, pile-soil kinematic interaction gradually increases bending moment from tip to head, with minor influence from superstructure’s inertial interaction. In hard soil sites, significant inertial effects from soil, even surpassing pile-soil kinematic effects near the tip, lead to reversed superposition bending moment. Superstructure’s inertial interaction notably impacts pile head in hard soil sites. A simplified coupling method is proposed using correlation coefficient to represent inertial and kinematic interactions. These findings provide insights into complex seismic interactions among soil, piles, and structures.

Though a comprehensive in situ measurement project, the performance of a deep pit-in-pit excavation constructed by the top-down method in seasonal frozen soil area in Shenyang was extensively examined. The measured excavation responses included the displacement of capping beam and retaining pile, settlement of ground surface, and deformation of metro lines. Based on the analyses of field data, some major findings were obtained: 1) the deformations of retaining structures fluctuated along with the increase of temperature, 2) the deformation variation of retaining structures after the occurrence of thawing of seasonal frozen soil was greater than that in winter, although the excavation depth was smaller than before, 3) the influence area of ground settlement was much smaller because of the features of seasonal frozen sandy soil, 4) the displacement of metro line showed a significant spatial effect, and the tunnel lining had an obviously hogging displacement pattern, and 5) earth pressure redistribution occurred due to the combined effects of freezing-thawing of seasonal frozen soil and excavation, leading to the deformation of metro line. The influence area of ground settlement was obviously smaller than that of Shanghai soft clay or other cases reported in literatures because of special geological conditions of Shenyang. However, the deformation of metro lines was significantly lager after the thawing of the frozen soil, the stress in deep soil was redistributed, and the metro lines were forced to deform to meet a new state of equilibrium.

This work uses isogeometric analysis (IGA), which is based on nonlocal hypothesis and higher-order shear beam hypothesis, to investigate the static bending and free oscillation of a magneto-electro-elastic functionally graded (MEE-FG) nanobeam subject to elastic boundary constraints (BCs). The magneto-electric boundary condition and the Maxwell equation are used to calculate the variation of electric and magnetic potentials along the thickness direction of the nanobeam. This study is innovative since it does not use the conventional boundary conditions. Rather, an elastic system of straight and torsion springs with controllable stiffness is used to support nanobeams’ beginning and end positions, creating customizable BCs. The governing equations of motion of nanobeams are established by applying Hamilton’s principle and IGA is used to determine deflections and natural frequency values. Verification studies were performed to evaluate the convergence and accuracy of the proposed method. Aside from this, the impact of the input parameters on the static bending and free oscillation of the MEE-FG nanobeam is examined in detail. These findings could be valuable for analyzing and designing innovative structures constructed of functionally graded MEE materials.

The precise prediction of the fundamental vibrational period for reinforced concrete (RC) buildings with infilled walls is essential for structural design, especially earthquake-resistant design. Machine learning models from previous studies, while boasting commendable accuracy in predicting the fundamental period, exhibit vulnerabilities due to lengthy training times and inherent dependence on pre-trained models, especially when engaging with continually evolving data sets. This predicament emphasizes the necessity for a model that adeptly balances predictive accuracy with robust adaptability and swift data training. The latter should include consistent re-training ability as demanded by real-time, continuously updated data sets. This research implements an optimized Light Gradient Boosting Machine (LightGBM) model, highlighting its augmented predictive capabilities, realized through the astute use of Bayesian Optimization for hyperparameter tuning on the FP4026 research data set, and illuminating its adaptability and efficiency in predictive modeling. The results show that the R² score of LightGBM model is 0.9995 and RMSE is 0.0178, while training speed is 23.2 times faster than that offered by XGBoost and 45.5 times faster than for Gradient Boosting. Furthermore, this study introduces a practical application through a streamlit-powered, web-based dashboard, enabling engineers to effortlessly utilize and augment the model, contributing data and ensuring precise fundamental period predictions, effectively bridging scholarly research and practical applications.

The stick-slip action of strike-slip faults poses a significant threat to the safety and stability of underground structures. In this study, the north-east area of the Longmenshan fault, Sichuan, provides the geological background; the rheological characteristics of the crustal lithosphere and the nonlinear interactions between plates are described by Burger’s viscoelastic constitutive model and the friction constitutive model, respectively. A large-scale global numerical model for plate squeezing analysis is established, and the seemingly periodic stick-slip action of faults at different crust depths is simulated. For a second model at a smaller scale, a local finite element model (sub-model), the time history of displacement at a ground level location on the Longmenshan fault plane in a stick-slip action is considered as the displacement loading. The integration of these models, creating a multi-scale modeling method, is used to evaluate the crack propagation and mechanical response of a tunnel subjected to strike-slip faulting. The determinations of the recurrence interval of stick-slip action and the cracking characteristics of the tunnel are in substantial agreement with the previous field investigation and experimental results, validating the multi-scale modeling method. It can be concluded that, regardless of stratum stiffness, initial cracks first occur at the inverted arch of the tunnel in the footwall, on the squeezed side under strike-slip faulting. The smaller the stratum stiffness is, the smaller the included angle between the crack expansion and longitudinal direction of the tunnel, and the more extensive the crack expansion range. For the tunnel in a high stiffness stratum, both shear and bending failures occur on the lining under strike-slip faulting, while for that in the low stiffness stratum, only bending failure occurs on the lining.

This study employs a hybrid approach, integrating finite element method (FEM) simulations with machine learning (ML) techniques to investigate the structural performance of double-skin tubular columns (DSTCs) reinforced with glass fiber-reinforced polymer (GFRP). The investigation involves a comprehensive examination of critical parameters, including aspect ratio, concrete strength, number of GFRP confinement layers, and dimensions of steel tubes used in DSTCs, through comparative analyses and parametric studies. To ensure the credibility of the findings, the results are rigorously validated against experimental data, establishing the precision and trustworthiness of the analysis. The present research work examines the use of the columns with elliptical cross-sections and contributes valuable insights into the application of FEM and ML in the design and evaluation of structural systems within the field of structural engineering.

The consideration of unsaturated conditions is infrequently addressed in current Terzaghi’s soil arching research. A modified analytical method for calculation of unsaturated loosening earth pressure above shallow trapdoor is proposed in this paper. By assuming the existence of a vertical slip surface above the trapdoor, the stress state of the soil in the loosening area are delineated in the extended Mohr–Coulomb circle. To account for the non-uniform distribution of vertical stress at arbitrary points along the horizontal differential soil trip, a virtual rotation circle trajectory of major principal stress is employed. Subsequently, the average vertical stress acting on the soil trip is determined through integral approach. Taking into account the influence of matric suction on soil weight and apparent cohesion, the differential equation governing the soil trip is solved analytically for cases of uniform matric suction distribution and alternatively using the finite difference method for scenarios involving non-uniform matric suction distribution. The proposed method’s validity is confirmed through comparison with published results. The parameter analysis indicates that the loosening earth pressure initially decreases and subsequently increases with the increase of the soil saturation. With the rise of groundwater level, the normalized effective loosening earth pressure shows a decreasing trend.

Collecting and analyzing vibration signals from structures under time-varying excitations is a non-destructive structural health monitoring approach that can provide meaningful information about the structures’ safety without interrupting their normal operations. This paper develops a novel framework using prompt engineering for seamlessly integrating users’ domain knowledge about vibration signals with the advanced inference ability of well-trained large language models (LLMs) to accurately identify the actual states of structures. The proposed framework involves formulating collected data into a standardized form, utilizing various prompts to gain useful insights into the dynamic characteristics of vibration signals, and implementing an in-house program with the help of LLMs to perform damage detection. The advantages, as well as limitations, of the proposed method are qualitatively and quantitatively assessed through two realistic case studies from literature, demonstrating that the present method is a new way to quickly construct practical and reliable structural health monitoring applications without requiring advanced programming/mathematical skills or obscure specialized programs.

The large vibrations of stay cables pose significant challenges to the structural performance and safety of cable-stayed bridges. While magnetorheological dampers (MRDs) have emerged as an effective solution for suppressing these vibrations, establishing accurate forward and inverse mapping models for MRDs to facilitate effective semi-active control of cable vibrations remains a formidable task. To address this issue, the current study proposes an innovative strategy that leverages Long Short-Term Memory (LSTM) neural networks for MRD modeling, thus enhancing semi-active control of stay cable vibrations. A high-fidelity data set accurately capturing the MRD dynamics is first generated by coupling finite element analysis and computational fluid dynamic approach. The obtained data set is then utilized for training LSTM-based forward and inverse mapping models of MRD. These LSTM models are subsequently integrated into dynamic computational models for effectively suppressing the stay cable vibrations, culminating in an innovative semi-active control strategy. The feasibility and superiority of the proposed strategy are demonstrated through comprehensive comparative analyses with existing passive, semi-active and active control methodologies involving sinusoidal load, Gaussian white noise load and rain–wind induced aerodynamic load scenarios, paving the way for novel solutions in semi-active vibration control of large-scale engineered structures.

Disaster-resilient dams require accurate crack detection, but machine learning methods cannot capture dam structural reaction temporal patterns and dependencies. This research uses deep learning, convolutional neural networks, and transfer learning to improve dam crack detection. Twelve deep-learning models are trained on 192 crack images. This research aims to provide up-to-date detecting techniques to solve dam crack problems. The finding shows that the EfficientNetB0 model performed better than others in classifying borehole concrete crack surface tiles and normal (undamaged) surface tiles with 91% accuracy. The study’s pre-trained designs help to identify and to determine the specific locations of cracks.

Sufficient survey data are required to describe the stochastic behaviors of live loads. However, due to manual and on-site operation required by traditional survey methods, traditional surveys face challenges like occupant resistance, high costs, and long implementation periods. This study proposes a new survey method to access live load data online and automatically. Required samples are acquired from multi-source, open-access and dynamically updated data on the Internet. The change intervals, geometrical dimensions and object quantities are obtained from transaction information, building attributes and virtual reality models on real estate websites, respectively. The object weights are collected from commodity information on e-commerce websites. The integration of the aforementioned data allows for the extraction of necessary statistics to describe a live load process. The proposed method is applied to a live load survey in China, covering 20040 m², with around 90000 samples acquired for object weights and load changes. The survey results reveal that about 70%−80% of the amplitude statistics are attributable to 1/6 of the total object types.